Utilization of location and environment to improve recognition

ABSTRACT

A portable terminal has a network interface that receives a set of instructions having a sequence of at least one location and audio properties associated with the at least one location from a server. An audio circuit receives audio signals picked up by a microphone and processes the audio signals in a manner defined by the audio properties associated with the at least one location. A speech recognition module receives processed signals from the audio circuit and carries out a speech recognition process thereupon.

FIELD OF THE INVENTION

The present invention relates to improvements in speech recognition.

BACKGROUND

In environments where mobile devices are performing voice recognition,many factors in the environment can negatively impact speech recognitionperformance. For example, when such systems are utilized in anenvironment wherein the ambient noise level changes from location tolocation (e.g., on a factory or warehouse floor) the ability of themobile device to perform accurate speech recognition can vary dependingupon the user's proximity to noise sources such as production machinery.

Therefore, a need exists for a mechanism to cope with variable sourcesof audible noise that interferes with accurate speech recognition.

SUMMARY

Accordingly, in one aspect, the present invention embraces a mechanismfor changing gain and other audio system characteristics based uponlocation of the portable device.

In an example embodiment, a device has a network interface that receivesa set of instructions from a server, the instructions comprising asequence of at least one location and audio properties associated withthe at least one location. An audio circuit receives audio signalspicked up by a microphone and processes the audio signals in a mannerdefined by the audio properties associated with the at least onelocation. A speech recognition module receives processed signals fromthe audio circuit and carries out a speech recognition processthereupon.

In accord with certain example embodiments, audio signals picked up bythe microphone are stored and conveyed to a server. In accord withcertain example embodiments, the speech recognition module utilizes auser template that characterizes speech of a particular user to enhancerecognition accuracy. In accord with certain example embodiments, theaudio circuit comprises an amplifier and where the gain of the amplifieris set by the audio properties for the at least one location. In accordwith certain example embodiments, the audio circuit comprises a noisecomparison circuit that compares the audio with a noise model defined bythe audio properties, and where the audio from the microphone isdiscarded if the audio matches the noise model. In accord with certainexample embodiments, the audio properties for the at least one locationare loaded after receiving a confirmation that the terminal has arrivedat the at least one location. In accord with certain exampleembodiments, the confirmation comprises an audio signal picked up by themicrophone. In accord with certain example embodiments, a speechsynthesizer synthesizes speech instruction from the set of instructions.

In another example embodiment, a portable terminal has a wirelessnetwork interface that receives a set of instructions from a server, theinstructions comprising a sequence of at least one location and audioproperties associated with the at least one location. An audio circuitreceives audio signals picked up by a microphone and processes the audiosignals in a manner defined by the audio properties associated with theat least one location. The audio circuit has an amplifier and the gainof the amplifier is set by the audio properties for the at least onelocation. The audio circuit may include a noise comparison circuit thatcompares the audio with a noise model defined by the audio properties,and where the audio is discarded if the audio matches the noise model. Aspeech recognition module receives processed signals from the audiocircuit and carries out a speech recognition process thereupon. A speechsynthesizer synthesizes speech instruction from the set of instructions.

In accord with certain example embodiments, audio signals picked up bythe microphone are stored and conveyed to a server. In accord withcertain example embodiments, the speech recognition module utilizes auser template that characterizes speech of a particular user to enhancerecognition accuracy. In accord with certain example embodiments, theaudio properties for the at least one location are loaded afterreceiving a confirmation that the terminal has arrived at the at leastone location. In accord with certain example embodiments, theconfirmation comprises an audio signal picked up by the microphone.

In another example embodiment, a method of processing speech signals ata portable terminal involves: receiving a set of instructions from aserver; the set of instructions include at least one location, a set ofactions to be carried out at the at least one location, and a set ofaudio processing parameters associated with the at least one location;synthesizing a speech command to proceed to the at least one location;receiving a speech signal from a microphone confirming arrival at the atleast one location; loading the audio processing parameters associatedwith the at least one location; and processing speech signals receivedfrom the microphone using the audio processing parameters associatedwith the at least one location.

In certain example methods, audio signals picked up by the microphoneare stored. In accord with certain example embodiments, the speechrecognition module utilizes a user template that characterizes speech ofa particular user to enhance speech recognition accuracy. In accord withcertain example embodiments, the audio processing parameters include anamplifier gain, and where the amplifier gain establishes the gain of anamplifier that amplifies signals from the microphone. In accord withcertain example embodiments, the method further involves comparing audiosignals received at the microphone with a noise model defined by theaudio processing parameters, and where the audio from the microphone isdiscarded if the audio matches the noise model. In accord with certainexample embodiments, the audio processing parameters include at leastone of a compression value, and a frequency response parameter thatprocesses signals from the microphone. In accord with certain exampleembodiments, the audio properties for the at least one location areloaded after receiving a confirmation that the terminal has arrived atthe at least one location.

The foregoing illustrative summary, as well as other exemplaryobjectives and/or advantages of the invention, and the manner in whichthe same are accomplished, are further explained within the followingdetailed description and its accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example system consistent with certain illustrativeembodiments.

FIG. 2 depicts an example of a set of pick instructions consistent withcertain illustrative embodiments.

FIG. 3 depicts an example flow chart of overall operation consistentwith certain illustrative embodiments.

FIG. 4 depicts an example flow chart consistent with certainillustrative embodiments.

FIG. 5 depicts an example flow chart for update of noise modelsconsistent with certain illustrative embodiments.

FIG. 6 depicts an example block diagram of a portable terminalconsistent with certain illustrative embodiments.

DETAILED DESCRIPTION

The present invention embraces a mechanism for utilizing workflowprogression information to control various audio characteristics forprocessing a received speech signal from a user for further processingby speech recognition components.

In an exemplary embodiment, a system such as the Vocollect™ systemproduced by Honeywell International, Inc. (e.g., including a portabledevice) is utilized to conduct various transactions. In one example, asdepicted by system 10 in FIG. 1, a user 14 utilizes a wireless portableterminal 18 that is coupled by a wireless network 22 to a server 26. Thewireless portable terminal is linked (e.g., via a wired or wirelessconnection such as a BlueTooth™ connection) to a headset 30 or otherearpiece arrangement that incorporates a microphone 34. Unfortunately,while microphone 34 is used for the user 14 to convey information to thewireless portable terminal 18, microphone 34 is also susceptible toreceiving ambient noises that may change from location to location. Thepresent teachings address this issue.

In one example, the system 10 may be utilized in a warehouse orproduction floor to provide working instructions for user 14. Forexample, user 14 may be responsible for picking items from shelves in awarehouse to help fulfill a customer's order. In another example, user14 may be responsible for picking production parts and delivering themto other workers on a factory floor. In either case, the user works from“pick instructions” conveyed by the terminal 18 to the user 14.

While in the usage area (e.g., a warehouse), the wireless portableterminal 18 communicates with server 26 to transfer many types ofinformation. If the terminal 18 knows its location or workflowprogression at all times, it can relay information about its locationalong with the aforementioned data. One example of this data tagged withthis location information could be the local noise levels or an actualaudio sampling of the noise.

As user 14 operates and moves about the area, the system can ‘learn’where the noise levels are highest and lowest, as well as what thecharacteristics of that noise are, and adapt itself accordingly toimprove recognition. For instance, knowing that a certain area isparticularly noisy, the system can automatically adjust the input gainapplied to signals from microphone 34 and/or adjust a noise model tobetter cope with the ambient noise levels in the environment.

In one example embodiment that is common for use of this type of system,the user 14 starts off receiving a set of pick instructions from theserver which might include, for example, the task of picking three itemsas follows:

Pick quantity 2 from aisle 7, bin 4.

Pick quantity 1 from aisle 8, bin 43.

Pick quantity 3 from aisle 12, bin 77.

It is noted that the present teachings are not limited to “pickinstructions” per se, but rather are applicable to any set ofinstructions that are used to direct a user 14 about to accomplish atask or to any situation where a user is moving around and utilizingspeech recognition. Returning to this example, this pick information isconveyed to the wireless portable terminal 18 which aids the user 14 incompleting the pick by first telling the user (by speech synthesisand/or display) to first proceed to aisle 7. The message might be “go toaisle 7 and then say ‘ready’”. The user 14 can then proceed to aisle 7and acknowledge arrival at that location (isle 7) by saying “ready” intomicrophone 34. At this point, the wireless portable terminal 18 willknow that the user is at aisle 7. In one embodiment, at this point theterminal 18 can monitor the ambient sounds for use in characterizing theenvironment of aisle 7. Data representing the ambient noise at aisle 7can be stored for later transmission back to the server 26 or may beimmediately sent back to server 26 if the network connection is solid,or queued to be sent later.

Also, once the user has confirmed the location (isle 7), if the locationis known to have a high background noise level, the terminal 18 canreduce the audio gain, adjust frequency response, adjust compression orutilize one or more a noise models to improve the quality of the speechrecognition. This knowledge, in certain examples, comes as a part of thepick instructions directing the user 14 to each particular location.

Once the user 14 has confirmed his or her location at aisle 7, terminal18 can provide instructions to go to bin 4 with an instruction such as“go to bin 4 and then say ‘ready’”. The user acknowledges arrival bysaying “ready”. When the user's location is refined (i.e., the user isnow at bin 4), the audio characteristics may be further adjusted ifdesired (or, a single set of characteristics can be used for the entireaisle). For example, one end of aisle 7 may be close to a noisy machinewhile the other end of the aisle may be considerably quiet. In otherexamples, an average can be used for each general location or the noiselevel can be characterized with any degree of precision desired.

Once the user has acknowledged arrival by saying “ready”, the terminal18 provides the instruction “pick quantity 2 and then say ‘ready’”. Whenthe user says “ready”, the terminal 18 proceeds to the second location(isle 8, bin 43) and after that provides a similar set of instructionsfor the third location (isle 12, bin 77). After the pick is completed,the user may receive a final instruction to deliver all items to aparticular location. Once that is accomplished, the user may again say“ready” and a new pick instruction set will be downloaded to theterminal 18 from the server 26.

When one or more noise models are used in the above example, the noisemodel(s) may define the characteristics of noise present at a particularlocation in such a manner that when the terminal 18 receives an audiosignal the signal is first checked against the noise model. If there isa match to the noise model, the audio is presumed invalid and is markedas noise and not interpreted as spoken words.

While ideally, every possible location might be characterized, inpractice it may be that significant benefit is only obtained bycharacterizing a few of the noisier locations in a particularenvironment.

With reference to FIG. 2, an example set of pick instructions isdepicted as 50. As previously noted, these instructions can be receivedprior to the user beginning the pick operation. The pick instructions asshown include one or more locations such as 54 (e.g., aisle 4); a set ofinstructions 54 (e.g., pick 4 from bin 7); and settings as they relateto each individual location in the instruction set. As depicted, twosettings are shown for each of the three locations A, B and C. Eachlocation has a gain 62 which represents the gain or gain adjustment thatshould be applied to the microphone audio and a noise model 66 thatcharacterizes the noise at each location. This information can beupdated each time a worker visits a particular location or may remainrelatively static over time.

It is noted that the changes that can be made to adapt to variouslocations is not limited to gain settings or a single noise model. Gain,frequency response, compression settings, and noise models are among theoptions of characteristics of audio processing that can be manipulatedin accord with the present teachings. Moreover, even within a noisemodel, variables might be adjusted, as opposed to replacing the completemodel (though that could be considered equivalent). Even in the searchalgorithms of the recognizer the weighting of noise models relative tothe rest of the words that are being matched can be adjusted to make itmore likely that a noise model will be accepted as a “match” to audiooverall, even if one did not change anything about the noise model.

The word “terminal” as used herein can be interpreted as a wirelessheadset that is connected to a processor that is not portable. In suchexample, the microphone is moving around with the user, but not theprocesser (recognizer or dialog engine). In other embodiments, a similarsystem could be implemented without WiFi, in which case the user plugs aheadset in at the start, get the information, do your route, then plugin afterwards to upload the results of the route. Many variations willoccur to those skilled in the art without departing from the presentteachings.

FIG. 3 shows an example of a process 100 flow chart for the processdescribed above starting at 102. The portable terminal 18 is booted upat 106 and initialized to be prepared for normal operation. In oneexample implementation, the speech recognition system can be renderedhighly reliable by being individualized to each user 14 and utilize alimited vocabulary. In such a system, the user 14 trains the terminal 18and if the device is shared, each user enters an identifier thatidentifies the user to the terminal 18. The terminal can then download atemplate representing the speech pattern of each particular user.

Speech recognition can also be made more reliable by using a limitedvocabulary—for example, “start”, “ready”, “complete”, “back” andnumerical digits 0-9 and perhaps a few additional commands.

Since the reliability of the speech recognition is desirably very high,and further desirably carried out at the portable terminal 18, limitedvocabulary and individualized training are desirable, but should not beconsidered limiting on the present teachings.

If the system is individualized to each user, the user's particularspeech recognition template that characterizes the user's speech can bedownloaded at 110 from the server to the portable device. After this, aset of pick instructions (or other instructions containing locations andaudio settings relating to each or certain of the locations isdownloaded at 114. The pick operation (for example) can then beginstarting with the first destination at 118. At 122, the portableterminal generates a speech (and/or displayed) instructions for the userthat instructs the user to go to this destination. The user can thenproceed to go to the designation location and confirms arrival at thatlocation when he or she arrives at 126.

Now that the terminal 18 knows that it has arrived at the designatedlocation, terminal 18 can load and apply gain and noise modelinformation from the pick instructions at 130 for use until the usermoves to a new location. The portable terminal and the user can nowproceed with a dialog in which the terminal 18 conveys instructions tothe user 14 at 134 in the form of speech and/or displayed text tellingthe user 14 what operation(s) to carry out at the location and in whichthe user provides confirmations that are used to confirm completion ofactions at 138. The sequence of 134 and 138 may repeated as the userproceeds through the dialog.

Once the user has completed the pick operation and has acknowledged such(e.g., by saying “ready”), the portable terminal determines if thedestination is the last in the pick instruction at 142. If not, theprocess proceeds to the next destination at 146 and control passes backto 122 to advance to the next destination. But, if the last destinationhas been processed at 142, the terminal generates instructions to returnto a designated area (e.g., a shipping department) with the picked itemsat 150. Further instructions may be generated as required for aparticular setting. At this point, the user may also receive a new setof pick instructions at 154 and the process begins again for the newpick instructions starting at 114. Many variations will occur to thoseskilled in the art upon consideration of the present teachings.

Turning now to FIG. 4, as previously discussed the portable terminal 18reads and parses the pick instructions to determine a sequence ofdestinations and gain and noise models for each location at 204. Then,for each location at 208, speech is synthesized at 212 to instruct theuser to go to the current location. The audio processing parameters arethen set to the associated gain and noise models for the location at216. A dialog based sequence of speech synthesis and speech recognitioncan then be carried out at 220 to complete the operation at the currentlocation. If this is not the last location at 224, the next location issimilarly processed starting at 212 as shown at 228. If the lastdestination has been reached at 224, the process returns at 232.

In the present examples, gain and noise model processing has beendescribed, but other audio processing could also be implemented andadjusted by location (e.g., equalization, amplitude compression,filtering, etc.) without limitation.

Referring to FIG. 5, it is noted that in some example audio environmentsthe noise may change based on any number of factors. For example, in amanufacturing environment certain machinery may only operate duringcertain times. If the times can be reliably predicted, this can be usedto determine how to best process the audio. In other instances, thenoise may gradually change over time and it is useful to dynamicallymodify the audio processing based upon the most recently gathered data.This can be accomplished in a number of ways including, as shown in FIG.5, at 250 detecting audio from the microphone. This audio can be sent tothe server (or stored for later transmission to the server) at 254 sothat the server can refine the gain and noise model (and any other audiocharacteristics) for use by the user or users. The audio can be timestamped to allow for associating a time to a noise environment seen atthat particular time. The audio that is received by the microphone isalso compared to the current noise model at 258 to determine if theaudio appears to match noise at 262. If so, the audio can be discardedat 266 and the process returns to 250. If no match at 262, the audio canbe further processed by the current gain and other processing includingspeech recognition and control returns to 250.

In carrying out this process, the currently received audio can be usedlocally at the portable terminal 18 in the processing by calculating again and noise model, for example, based on the currently receivedaudio. This calculated gain and noise model can be used if significantlydifferent than that stored for the particular location in certainembodiments. In other embodiments, the actual gain may be the average ofthat calculated and that received with the pick instruction. The noisemodel used may similarly be a combination of the noise model saved withthe pick instructions and the currently calculated noise model. Manyvariations will occur to those skilled in the art upon consideration ofthe present teachings.

FIG. 6 depicts a functional block diagram of an example of a portablewireless terminal 18 coupled via a Bluetooth transceiver or otherwireless or wired transceiver 302 to a paired headset 30 with microphone34. The terminal 18 includes a processor unit (e.g., a CPU) 308 havingassociated non-volatile memory 312 and Random Access Memory (RAM) 316via one or more bus connections 320. The bus 320 is further connected toa WiFi transceiver 324 for communication with the wireless network 22. Auser interface 328 is also provided which may include a variety of usercontrols and displays as is appropriate to the device.

The functions discussed above are carried out by processor 308 utilizingprogramming stored in the memory 312 and 316. In this example,particular functional modules are depicted in RAM 316 that representvarious functions discussed. Operating system 350 carries out thefunctions normally associated with an operating system (e.g., Linux orAndroid). The speech recognition module 354 carries out speechprocessing to convert speech received via the microphone 34 to a messageunderstood by the terminal 18. The speech synthesis module 358 generatessynthesized speech that is conveyed to the user via headset 30. The usertemplate 362 provides information that is used by the speech recognitionmodule 354 to improve the accuracy of recognition of speech by aparticular user. Pick instructions are stored as data at 366 for use asdescribed by parsing the instructions to generate speech and to loadvarious audio processing parameters used by audio processing module 370in conjunction with other audio circuits such as 374 to affect gain,noise model, etc. The speech recognition module may be implemented as ahardware module or as a processor utilizing speech recognition processesdefined by 354. Many variations are possible without departing from thepresent teachings.

In the present embodiments, the location information is first providedby the server in the pick instructions and confirmed by the user uponarrival at the location. In certain embodiments, the locationinformation can also be provided by or supplemented by GPS data using aGPS receiver forming a part of the terminal 18 (not shown) or otherposition determination mechanisms without limitation. The GPSinformation can be used to enhance the accuracy of the user's locationor can be used independently without limitation.

In accord with certain embodiments, each location may not have to becharacterized for audio parameters. The audio parameters may berepresented as deviations from a normal setting (e.g., instructions onhow much gain to add or subtract from normal), and the normal setting(e.g., gain) may be suitable for a wide variety of inputs.

In certain example embodiments, the usual workflow is that the terminalprompts “go to aisle X and then say ready” and then “go to bin Y andthen say ready”. The audio characteristics are applied according to oneexample at the location after the user confirms that location. However,variations can be implemented without departing from the presentteachings.

To supplement the present disclosure, this application incorporatesentirely by reference the following commonly assigned patents, patentapplication publications, and patent applications:

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While the present discussion uses example embodiments shown as flowcharts, equivalent hardware equivalents are also possible. Also, theorder of certain operations of the flow charts may be modified withoutdeparting from the present teachings.

In the specification and/or figures, typical embodiments of theinvention have been disclosed. The present invention is not limited tosuch exemplary embodiments. The use of the term “and/or” includes anyand all combinations of one or more of the associated listed items. Thefigures are schematic representations and so are not necessarily drawnto scale. Unless otherwise noted, specific terms have been used in ageneric and descriptive sense and not for purposes of limitation.

The invention claimed is:
 1. A portable terminal, comprising: a wirelessnetwork interface configured to receive, from a server, a set ofinstructions comprising at least one location, a set of actions to becarried out by a user at the at least one location, and a set of audioprocessing parameters associated with the at least one location; aspeech synthesizer configured to synthesize a speech command to proceedto the at least one location; an audio circuit configured to receiveaudio signals picked up by a microphone and processes the audio signals;a speech recognition module configured to receive processed signals fromthe audio circuit and carry out a speech recognition process thereuponto confirm arrival of the user at the at least one location; and aprocessor configured to, in response to the speech recognition modulecarrying out the speech recognition process to confirm the arrival ofthe user at the at least one location, load the set of audio processingparameters associated with the at least one location, so that speechsignals received from the microphone are processed using the set ofaudio processing parameters associated with the at least one location.2. The terminal according to claim 1, where audio signals picked up bythe microphone are stored and conveyed to a server.
 3. The terminalaccording to claim 1, where the speech recognition module utilizes auser template that characterizes speech of a particular user to enhancerecognition accuracy.
 4. A method of processing speech signals at aportable terminal, comprising: receiving a set of instructions from aserver, the set of instructions comprising at least one location, a setof actions to be carried out at the at least one location, and a set ofaudio processing parameters associated with the at least one location;synthesizing a speech command to proceed to the at least one location;receiving a speech signal from a microphone confirming arrival at the atleast one location; loading the audio processing parameters associatedwith the at least one location; and processing speech signals receivedfrom the microphone using the audio processing parameters associatedwith the at least one location.
 5. The method according to claim 4,where audio signals picked up by the microphone are stored.
 6. Themethod according to claim 4, where the speech recognition moduleutilizes a user template that characterizes speech of a particular userto enhance speech recognition accuracy.
 7. The method according to claim4, where the audio processing parameters include an amplifier gain, andwhere the amplifier gain establishes the gain of an amplifier thatamplifies signals from the microphone.
 8. The method according to claim4, further comprising comparing audio signals received at the microphonewith a noise model defined by the audio processing parameters, and wherethe audio from the microphone is discarded if the audio matches thenoise model.
 9. The method according to claim 4, where the audioprocessing parameters include at least one of a compression value, and afrequency response parameter that processes signals from the microphone.10. The method according to claim 4, where the audio properties for theat least one location are loaded after receiving a confirmation that theterminal has arrived at the at least one location.
 11. A method ofprocessing speech signals at a portable terminal, comprising: receivinga set of instructions from a server, the set of instructions comprisingat least one location, a set of actions to be carried out at the atleast one location, and a set of audio processing parameters associatedwith the at least one location; synthesizing a speech command to proceedto the at least one location; receiving a speech signal from amicrophone confirming arrival at the at least one location; loading, inresponse to the receiving of the speech signal from the microphoneconfirming arrival at the at least one location, the audio processingparameters associated with the at least one location; and thenprocessing speech signals received from the microphone using the audioprocessing parameters associated with the at least one location.
 12. Themethod according to claim 11, where audio signals picked up by themicrophone are stored.
 13. The method according to claim 11, where thespeech recognition module utilizes a user template that characterizesspeech of a particular user to enhance speech recognition accuracy. 14.The method according to claim 11, where the audio processing parametersinclude an amplifier gain, and where the amplifier gain establishes thegain of an amplifier that amplifies signals from the microphone.
 15. Themethod according to claim 11, further comprising comparing audio signalsreceived at the microphone with a noise model defined by the audioprocessing parameters, and where the audio from the microphone isdiscarded if the audio matches the noise model.
 16. The method accordingto claim 11, where the audio processing parameters include at least oneof a compression value, and a frequency response parameter thatprocesses signals from the microphone.
 17. The method according to claim11, where the audio properties for the at least one location are loadedafter receiving a confirmation that the terminal has arrived at the atleast one location.